{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial Audio Examples\n",
    "\n",
    "This notebook demonstrates how to use the ART library to create adversarial audio examples.\n",
    "\n",
    "---\n",
    "\n",
    "## Preliminaries\n",
    "\n",
    "Before diving into the different steps necessary, we walk through some initial work steps ensuring that the notebook will work smoothly. We will \n",
    "\n",
    "1. set up a small configuration cell, \n",
    "2. check if the test data and pre-trained model are available or otherwise download them\n",
    "3. define some necessary Python classes to handle the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchaudio\n",
    "\n",
    "\n",
    "from art.attacks import ProjectedGradientDescent\n",
    "from art.classifiers import PyTorchClassifier\n",
    "from art.config import ART_DATA_PATH\n",
    "from art.utils import get_file\n",
    "\n",
    "OUTPUT_SIZE = 8000\n",
    "ORIGINAL_SAMPLING_RATE = 48000\n",
    "DOWNSAMPLED_SAMPLING_RATE = 8000\n",
    "\n",
    "# set global variables\n",
    "AUDIO_DATA_PATH = os.path.join(ART_DATA_PATH, \"audiomnist/test\")\n",
    "AUDIO_MODEL_PATH = os.path.join(ART_DATA_PATH, \"adversarial_audio_model.pt\")\n",
    "\n",
    "# set seed\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download AudioMNIST data and pretrained model\n",
    "get_file('adversarial_audio_model.pt', 'https://www.dropbox.com/s/iccucsrs6wu7gbb/model_raw_audio_202002260446.pt?dl=1')\n",
    "get_file('audiomnist.tar.gz', 'https://api.github.com/repos/soerenab/AudioMNIST/tarball');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash -s \"$AUDIO_DATA_PATH\" \"$ART_DATA_PATH\"\n",
    "mkdir -p $1\n",
    "tar -xf $2/audiomnist.tar.gz \\\n",
    "    -C $1 \\\n",
    "    --strip-components=2 */data \\\n",
    "    --exclude=**/*/{01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataloader and preprocessing classes.\n",
    "class AudioMNISTDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"Dataset object for the AudioMNIST data set.\"\"\"\n",
    "    def __init__(self, root_dir, transform=None, verbose=False):\n",
    "        self.root_dir = root_dir\n",
    "        self.audio_list = glob.glob(f\"{root_dir}/*/*.wav\")\n",
    "        self.transform = transform\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.audio_list)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        audio_fn = self.audio_list[idx]\n",
    "        if self.verbose:\n",
    "            print(f\"Loading audio file {audio_fn}\")\n",
    "        waveform, sample_rate = torchaudio.load_wav(audio_fn)\n",
    "        if self.transform:\n",
    "            waveform = self.transform(waveform)\n",
    "        sample = {\n",
    "            'input': waveform,\n",
    "            'digit': int(os.path.basename(audio_fn).split(\"_\")[0])\n",
    "        }\n",
    "        return sample\n",
    "\n",
    "\n",
    "class PreprocessRaw(object):\n",
    "    \"\"\"Transform audio waveform of given shape.\"\"\"\n",
    "    def __init__(self, size_out=OUTPUT_SIZE, orig_freq=ORIGINAL_SAMPLING_RATE,\n",
    "                 new_freq=DOWNSAMPLED_SAMPLING_RATE):\n",
    "        self.size_out = size_out\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        transformed_waveform = _ZeroPadWaveform(self.size_out)(\n",
    "            _ResampleWaveform(self.orig_freq, self.new_freq)(waveform)\n",
    "        )\n",
    "        return transformed_waveform\n",
    "\n",
    "\n",
    "class _ResampleWaveform(object):\n",
    "    \"\"\"Resample signal frequency.\"\"\"\n",
    "    def __init__(self, orig_freq, new_freq):\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._resample_waveform(waveform)\n",
    "\n",
    "    def _resample_waveform(self, waveform):\n",
    "        resampled_waveform = torchaudio.transforms.Resample(\n",
    "            orig_freq=self.orig_freq,\n",
    "            new_freq=self.new_freq,\n",
    "        )(waveform)\n",
    "        return resampled_waveform\n",
    "\n",
    "\n",
    "class _ZeroPadWaveform(object):\n",
    "    \"\"\"Apply zero-padding to waveform.\n",
    "\n",
    "    Return a zero-padded waveform of desired output size. The waveform is\n",
    "    positioned randomly.\n",
    "    \"\"\"\n",
    "    def __init__(self, size_out):\n",
    "        self.size_out = size_out\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._zero_pad_waveform(waveform)\n",
    "\n",
    "    def _zero_pad_waveform(self, waveform):\n",
    "        padding_total = self.size_out - waveform.shape[-1]\n",
    "        padding_left = np.random.randint(padding_total + 1)\n",
    "        padding_right = padding_total - padding_left\n",
    "        padded_waveform = torch.nn.ConstantPad1d(\n",
    "            (padding_left, padding_right),\n",
    "            0\n",
    "        )(waveform)\n",
    "        return padded_waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_waveform(waveform, title=\"\", sr=8000):\n",
    "    \"\"\"Display waveform plot and audio play UI.\"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    plt.plot(waveform)\n",
    "    ipd.display(ipd.Audio(waveform, rate=sr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model and Test Data\n",
    "\n",
    "In the following section we are going to load the pretrained model that we downloaded in the previous section. Let's also load the test data set from which we will generate adversarial examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load AudioMNIST test set\n",
    "audiomnist_test = AudioMNISTDataset(\n",
    "    root_dir=AUDIO_DATA_PATH,\n",
    "    transform=PreprocessRaw(),\n",
    ")\n",
    "\n",
    "# load pretrained model\n",
    "model = torch.load(\n",
    "    AUDIO_MODEL_PATH,\n",
    "    map_location=\"cpu\",\n",
    ")\n",
    "model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Adversarial Examples\n",
    "\n",
    "After loading the test set and model, we are ready to employ the ART library. We will first load a sample, which here will have label 1. The classification model correctly classifies it as such. We will then use ART and perform a Projected Gradient Descent attack. The attack will corrupt the spoken audio and will be misclassified as 9. However, there is almost no hearable difference in the original audio file and the adversarial audio file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wrap model in a ART classifier\n",
    "classifier_art = PyTorchClassifier(\n",
    "    model=model,\n",
    "    loss=torch.nn.CrossEntropyLoss(),\n",
    "    optimizer=None,\n",
    "    input_shape=[1, DOWNSAMPLED_SAMPLING_RATE],\n",
    "    nb_classes=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction (ground truth):\t1 (1)\n",
      "Adversarial prediction:\t\t\t9\n"
     ]
    }
   ],
   "source": [
    "# load a test sample\n",
    "sample = audiomnist_test[1]\n",
    "\n",
    "waveform = sample['input']\n",
    "label = sample['digit']\n",
    "\n",
    "# craft adversarial example with PGD\n",
    "epsilon = .2\n",
    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
    "adv_waveform = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that for the given test sample, the model correctly classified it as **1**. Applying PGD, we are able to create an adversarial example that is now classified as **9**.\n",
    "\n",
    "Now we can qualitatively explore the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display adversarial example\n",
    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display original example\n",
    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a second adversarial example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction (ground truth):\t5 (5)\n",
      "Adversarial prediction:\t\t\t6\n"
     ]
    }
   ],
   "source": [
    "# load a test sample\n",
    "sample = audiomnist_test[444]\n",
    "\n",
    "waveform = sample['input']\n",
    "label = sample['digit']\n",
    "\n",
    "# craft adversarial example with PGD\n",
    "epsilon = 0.5\n",
    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
    "adv_waveform = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we observe that for the given test sample, the model correctly classified it as **5**. Applying PGD, we are able to create an adversarial example that is now classified as **6**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display adversarial example\n",
    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display original example\n",
    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We present a final third example. For this example observe that the model correctly classifies it as **8**, but the adversarial example is classified as **3**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction (ground truth):\t8 (8)\n",
      "Adversarial prediction:\t\t\t3\n"
     ]
    }
   ],
   "source": [
    "# load a test sample\n",
    "sample = audiomnist_test[1021]\n",
    "\n",
    "waveform = sample['input']\n",
    "label = sample['digit']\n",
    "\n",
    "# craft adversarial example with PGD\n",
    "epsilon = 0.35\n",
    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
    "adv_waveform = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display adversarial example\n",
    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display original example\n",
    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this notebook we have demonstrated how we can apply the ART library to audio data. By providing a pretrained PyTorch model and loading it via ART's `PyTorchClassifier` we can easily plug in several off the shelf attacks like Projected Gradient Descent.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduce CNN\n",
    "\n",
    "Our goal is to make it as easy as possible to reproduce or modify our modified AudioMNIST classifier, which we provided as a pretrained fixture in the notebook. Therefore, we provide in the following the original code that we used for training the AudioMNIST classifier.\n",
    "\n",
    "**Training script `train.py`**\n",
    "\n",
    "```python\n",
    "#!/usr/bin/env python\n",
    "\n",
    "\"\"\"Train a simple AudioNet-like classifier for AudioMNIST.\"\"\"\n",
    "\n",
    "import logging\n",
    "import time\n",
    "\n",
    "import torch\n",
    "\n",
    "from dataloader import AudioMNISTDataset, PreprocessRaw\n",
    "from model import RawAudioCNN\n",
    "\n",
    "# set global variables\n",
    "AUDIO_DATA_TRAIN_ROOT = \"data/audiomnist/train\"\n",
    "AUDIO_DATA_TEST_ROOT = \"data/audiomnist/test\"\n",
    "\n",
    "\n",
    "def _is_cuda_available():\n",
    "    return torch.cuda.is_available()\n",
    "\n",
    "\n",
    "def _get_device():\n",
    "    return torch.device(\"cuda\" if _is_cuda_available() else \"cpu\")\n",
    "\n",
    "\n",
    "def main():\n",
    "    # Step 0: parse args and init logger\n",
    "    logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "    generator_params = {\n",
    "        'batch_size': 64,\n",
    "        'shuffle': True,\n",
    "        'num_workers': 6\n",
    "    }\n",
    "\n",
    "    # Step 1: load data set\n",
    "    train_data = AudioMNISTDataset(\n",
    "        root_dir=AUDIO_DATA_TRAIN_ROOT,\n",
    "        transform=PreprocessRaw(),\n",
    "    )\n",
    "    test_data = AudioMNISTDataset(\n",
    "        root_dir=AUDIO_DATA_TEST_ROOT,\n",
    "        transform=PreprocessRaw(),\n",
    "    )\n",
    "\n",
    "    train_generator = torch.utils.data.DataLoader(\n",
    "        train_data,\n",
    "        **generator_params,\n",
    "    )\n",
    "    test_generator = torch.utils.data.DataLoader(\n",
    "        test_data,\n",
    "        **generator_params,\n",
    "    )\n",
    "\n",
    "    # Step 2: prepare training\n",
    "    device = _get_device()\n",
    "    logging.info(device)\n",
    "\n",
    "    model = RawAudioCNN()\n",
    "    if _is_cuda_available():\n",
    "        model.to(device)\n",
    "\n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "    # Step 3: train\n",
    "    n_epochs = 60\n",
    "    for epoch in range(n_epochs):\n",
    "        # training loss\n",
    "        training_loss = 0.0\n",
    "        # validation loss\n",
    "        validation_loss = 0\n",
    "        # accuracy\n",
    "        correct = 0\n",
    "        total = 0\n",
    "\n",
    "        model.train()\n",
    "        for batch_idx, batch_data in enumerate(train_generator):\n",
    "            inputs = batch_data['input']\n",
    "            labels = batch_data['digit']\n",
    "            if _is_cuda_available():\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "            # Model computations\n",
    "            optimizer.zero_grad()\n",
    "            # forward + backward + optimize\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            # sum training loss\n",
    "            training_loss += loss.item()\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for batch_idx, batch_data in enumerate(test_generator):\n",
    "                inputs = batch_data['input']\n",
    "                labels = batch_data['digit']\n",
    "                if _is_cuda_available():\n",
    "                    inputs = inputs.to(device)\n",
    "                    labels = labels.to(device)\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                # sum validation loss\n",
    "                validation_loss += loss.item()\n",
    "                # calculate validation accuracy\n",
    "                predictions = torch.max(outputs.data, 1)[1]\n",
    "                total += labels.size(0)\n",
    "                correct += (predictions == labels).sum().item()\n",
    "\n",
    "        # calculate final metrics\n",
    "        validation_loss /= len(test_generator)\n",
    "        training_loss /= len(train_generator)\n",
    "        accuracy = 100 * correct / total\n",
    "        logging.info(f\"[{epoch+1}] train-loss: {training_loss:.3f}\"\n",
    "                     f\"\\tval-loss: {validation_loss:.3f}\"\n",
    "                     f\"\\taccuracy: {accuracy:.2f}\")\n",
    "    logging.info(\"Finished Training\")\n",
    "\n",
    "    # Step 4: save model\n",
    "    torch.save(\n",
    "        model,\n",
    "        f\"model/model_raw_audio_{time.strftime('%Y%m%d%H%M')}.pt\"\n",
    "    )\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "**Dataloader module `dataloader.py`:**\n",
    "\n",
    "```python\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchaudio\n",
    "\n",
    "\n",
    "OUTPUT_SIZE = 8000\n",
    "ORIGINAL_SAMPLING_RATE = 48000\n",
    "DOWNSAMPLED_SAMPLING_RATE = 8000\n",
    "\n",
    "\n",
    "class AudioMNISTDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"Dataset object for the AudioMNIST data set.\"\"\"\n",
    "    def __init__(self, root_dir, transform=None, verbose=False):\n",
    "        self.root_dir = root_dir\n",
    "        self.audio_list = glob.glob(f\"{root_dir}/*/*.wav\")\n",
    "        self.transform = transform\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.audio_list)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        audio_fn = self.audio_list[idx]\n",
    "        if self.verbose:\n",
    "            print(f\"Loading audio file {audio_fn}\")\n",
    "        waveform, sample_rate = torchaudio.load_wav(audio_fn)\n",
    "        if self.transform:\n",
    "            waveform = self.transform(waveform)\n",
    "        sample = {\n",
    "            'input': waveform,\n",
    "            'digit': int(os.path.basename(audio_fn).split(\"_\")[0])\n",
    "        }\n",
    "        return sample\n",
    "\n",
    "\n",
    "class PreprocessRaw(object):\n",
    "    \"\"\"Transform audio waveform of given shape.\"\"\"\n",
    "    def __init__(self, size_out=OUTPUT_SIZE, orig_freq=ORIGINAL_SAMPLING_RATE,\n",
    "                 new_freq=DOWNSAMPLED_SAMPLING_RATE):\n",
    "        self.size_out = size_out\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        transformed_waveform = _ZeroPadWaveform(self.size_out)(\n",
    "            _ResampleWaveform(self.orig_freq, self.new_freq)(waveform)\n",
    "        )\n",
    "        return transformed_waveform\n",
    "\n",
    "\n",
    "class _ResampleWaveform(object):\n",
    "    \"\"\"Resample signal frequency.\"\"\"\n",
    "    def __init__(self, orig_freq, new_freq):\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._resample_waveform(waveform)\n",
    "\n",
    "    def _resample_waveform(self, waveform):\n",
    "        resampled_waveform = torchaudio.transforms.Resample(\n",
    "            orig_freq=self.orig_freq,\n",
    "            new_freq=self.new_freq,\n",
    "        )(waveform)\n",
    "        return resampled_waveform\n",
    "\n",
    "\n",
    "class _ZeroPadWaveform(object):\n",
    "    \"\"\"Apply zero-padding to waveform.\n",
    "\n",
    "    Return a zero-padded waveform of desired output size. The waveform is\n",
    "    positioned randomly.\n",
    "    \"\"\"\n",
    "    def __init__(self, size_out):\n",
    "        self.size_out = size_out\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._zero_pad_waveform(waveform)\n",
    "\n",
    "    def _zero_pad_waveform(self, waveform):\n",
    "        padding_total = self.size_out - waveform.shape[-1]\n",
    "        padding_left = np.random.randint(padding_total + 1)\n",
    "        padding_right = padding_total - padding_left\n",
    "        padded_waveform = torch.nn.ConstantPad1d(\n",
    "            (padding_left, padding_right),\n",
    "            0\n",
    "        )(waveform)\n",
    "        return padded_waveform\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "**Model module `model.py`:**\n",
    "\n",
    "```python\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "class RawAudioCNN(nn.Module):\n",
    "    \"\"\"Adaption of AudioNet (arXiv:1807.03418).\"\"\"\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 1 x 8000\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv1d(1, 100, kernel_size=3, stride=1, padding=2),\n",
    "            nn.BatchNorm1d(100),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(3, stride=2))\n",
    "        # 32 x 4000\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv1d(100, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 64 x 2000\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 1000\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 500\n",
    "        self.conv5 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 250\n",
    "        self.conv6 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 125\n",
    "        self.conv7 = nn.Sequential(\n",
    "            nn.Conv1d(128, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 64 x 62\n",
    "        self.conv8 = nn.Sequential(\n",
    "            nn.Conv1d(64, 32, kernel_size=3, stride=1, padding=0),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.ReLU(),\n",
    "            # maybe replace pool with dropout here\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "\n",
    "        # 32 x 30\n",
    "        self.fc = nn.Linear(32 * 30, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.conv6(x)\n",
    "        x = self.conv7(x)\n",
    "        x = self.conv8(x)\n",
    "        x = x.view(x.shape[0], 32 * 30)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "```\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
